Electronic messages have become an indispensable part of modern communication. Electronic messages such as email or instant messages are popular because they are fast, easy, and have essentially no incremental cost. Unfortunately, these advantages of electronic messages are also exploited by marketers who regularly send out unsolicited junk messages. The junk messages are referred to as “spam”, and spam senders are referred to as “spammers”. Spam messages are a nuisance to users. They clog email inboxes, waste system resources, often promote distasteful subjects, and sometimes sponsor outright scams.
Although there are many existing message classification systems capable of classifying spam messages, the current message classification systems typically cannot perfectly classify every message. Sometimes a legitimate email message may be erroneously classified as spam. These types of misidentifications are sometimes referred to as “false positives”. A variety of factors may lead to false positives. For example, a message may include certain keywords that would cause the spam filtering system to identify the message as spam, even though these words were used in a legitimate context. Although more sophisticated spam identification algorithms may reduce the rate of false positives, whether a message is spam is ultimately judged by the human recipient. However, requiring the user to make a classification for every message is impractical, and defeats the purpose of having a spam filtering system. It would be desirable to have a message handling system that could ameliorate the problem of false positive identifications without requiring significant effort by its users.